Radial wavelet networks have been proposed previously as a method for nonparametric regression. We analyze their performance within a Bayesian framework. We derive probability distributions over both the dimension of the networks and the network coefficients by placing a prior on the degrees of freedom of the model. This process bypasses the need to test or select a finite number of networks during the modeling process. Predictions are formed by mixing over many models of varying dimension and parameterization. We show that the complexity of the models adapts to the complexity of the data and produces good results on a number of benchmark test series.
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